Modeling properties of recycled aggregate concrete using gene expression programming and artificial neural network techniques

被引:0
作者
Awoyera, Paul O. [1 ,8 ]
Bahrami, Alireza [2 ]
Oranye, Chukwufumnanya [1 ,9 ]
Romero, Lenin M. Bendezu [3 ,10 ]
Mansouri, Ehsan [4 ,5 ]
Mortazavi, Javad [6 ]
Hu, Jong Wan [6 ,7 ]
机构
[1] Covenant Univ, Dept Civil Engn, Ota, Nigeria
[2] Univ Gavle, Fac Engn & Sustainable Dev, Dept Bldg Engn Energy Syst & Sustainabil Sci, Gavle, Sweden
[3] Univ Cesar Vallejo, Escuela Profess Ingn Cvil, San Juan De Lurigancho, Peru
[4] Birjand Univ Med Sci, Dept Informat Technol, Birjand, Iran
[5] Azad Univ, Birjand Branch, Birjand, Iran
[6] Incheon Natl Univ, Dept Civil & Environm Engn, Incheon, South Korea
[7] Incheon Natl Univ, Incheon Disaster Prevent Res Ctr, Incheon, South Korea
[8] Prince Mohammad Bin Fahd Univ, Dept Civil Engn, Dhahran, Saudi Arabia
[9] Covenant Univ, Dept Civil Engn, Ota, Nigeria
[10] Univ Peruana Ciencias Aplicadas, Dept Civil Engn, Lima, Peru
关键词
modeling; recycled aggregate concrete; artificial neural network; gene expression programming; strength properties; MECHANICAL-PROPERTIES; STRENGTH; PREDICTION;
D O I
10.3389/fbuil.2024.1447800
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Soft computing techniques have become popular for solving complex engineering problems and developing models for evaluating structural material properties. There are limitations to the available methods, including semi-empirical equations, such as overestimating or underestimating outputs, and, more importantly, they do not provide predictive mathematical equations. Using gene expression programming (GEP) and artificial neural networks (ANNs), this study proposes models for estimating recycled aggregate concrete (RAC) properties. An experimental database compiled from parallel studies, and a large amount of literature was used to develop the models. For compressive strength prediction, GEP yielded a coefficient of determination (R2) value of 0.95, while ANN achieved an R2 value of 0.93, demonstrating high reliability. The proposed predictive models are both simple and robust, enhancing the accuracy of RAC property estimation and offering a valuable tool for sustainable construction.
引用
收藏
页数:13
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